如何训练你的机器人？示教模态对模仿学习的影响
How to Train Your Robots? The Impact of Demonstration Modality on Imitation Learning
AI总结：
本文比较动觉示教、VR控制器遥操作和空间鼠标遥操作三种示教模态对模仿学习性能与用户体验的影响，发现动觉示教最直观且数据质量最高，但体力负担大，进而提出少量动觉示教与遥操作数据混合的采集方案以兼顾学习性能与采集效率。
摘要：
模仿学习是一种利用用户提供的数据学习机器人策略的有前景的方法。示教的提供方式，即示教模态，会影响数据的质量。现有研究表明，动觉示教（物理引导机器人）因其直观性和易用性而受到用户青睐，但现有的大多数操作数据集是通过VR控制器或空间鼠标进行遥操作采集的。在本工作中，我们研究不同的示教模态如何影响下游学习性能以及用户体验。具体而言，我们比较了低成本示教模态，包括动觉示教、使用VR控制器的遥操作以及使用空间鼠标控制器的遥操作。我们在三个具有不同运动约束的桌面操作任务上进行了实验。我们使用来自不同示教模态的数据评估和比较模仿学习性能，并收集了关于用户体验的主观反馈。我们的结果表明，动觉示教被认为在控制机器人方面最直观，并且为最佳下游学习性能提供了最干净的数据。然而，由于体力负担，它并不被偏好作为大规模数据采集的方式。基于这一见解，我们提出了一种简单的数据采集方案，该方案依赖少量动觉示教与通过遥操作采集的数据混合，以在保持较低数据采集工作量的同时实现最佳整体学习性能。
英文摘要：
Imitation learning is a promising approach for learning robot policies with user-provided data. The way demonstrations are provided, i.e., demonstration modality, influences the quality of the data. While existing research shows that kinesthetic teaching (physically guiding the robot) is preferred by users for the intuitiveness and ease of use, the majority of existing manipulation datasets were collected through teleoperation via a VR controller or spacemouse. In this work, we investigate how different demonstration modalities impact downstream learning performance as well as user experience. Specifically, we compare low-cost demonstration modalities including kinesthetic teaching, teleoperation with a VR controller, and teleoperation with a spacemouse controller. We experiment with three table-top manipulation tasks with different motion constraints. We evaluate and compare imitation learning performance using data from different demonstration modalities, and collected subjective feedback on user experience. Our results show that kinesthetic teaching is rated the most intuitive for controlling the robot and provides cleanest data for best downstream learning performance. However, it is not preferred as the way for large-scale data collection due to the physical load. Based on such insight, we propose a simple data collection scheme that relies on a small number of kinesthetic demonstrations mixed with data collected through teleoperation to achieve the best overall learning performance while maintaining low data-collection effort.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
